Update app.py

app.py

@@ -28,58 +28,58 @@ st.subheader('Feeling like you might need a better coping strategy? Take the qui
 ###       data transformation      ###
 ### ------------------------------ ###
 
-def train_model():
-  # load dataset
-  uncleaned_data = pd.read_csv('data.csv')
+
+# load dataset
+uncleaned_data = pd.read_csv('data.csv')
   
-  # remove timestamp from dataset (always first column)
-  uncleaned_data = uncleaned_data.iloc[: , 1:]
-  data = pd.DataFrame()
+# remove timestamp from dataset (always first column)
+uncleaned_data = uncleaned_data.iloc[: , 1:]
+data = pd.DataFrame()
   
-  # keep track of which columns are categorical and what 
-  # those columns' value mappings are
-  # structure: {colname1: {...}, colname2: {...} }
-  cat_value_dicts = {}
-  final_colname = uncleaned_data.columns[len(uncleaned_data.columns) - 1]
+# keep track of which columns are categorical and what 
+# those columns' value mappings are
+# structure: {colname1: {...}, colname2: {...} }
+cat_value_dicts = {}
+final_colname = uncleaned_data.columns[len(uncleaned_data.columns) - 1]
   
-  # for each column...
-  for (colname, colval) in uncleaned_data.iteritems():
+# for each column...
+for (colname, colval) in uncleaned_data.iteritems():
   
-    # check if col is already a number; if so, add col directly
-    # to new dataframe and skip to next column
-    if isinstance(colval.values[0], (np.integer, float)):
-      data[colname] = uncleaned_data[colname].copy()
-      continue
+  # check if col is already a number; if so, add col directly
+  # to new dataframe and skip to next column
+  if isinstance(colval.values[0], (np.integer, float)):
+    data[colname] = uncleaned_data[colname].copy()
+    continue
   
-    # structure: {0: "lilac", 1: "blue", ...}
-    new_dict = {}
-    val = 0 # first index per column
-    transformed_col_vals = [] # new numeric datapoints
+  # structure: {0: "lilac", 1: "blue", ...}
+  new_dict = {}
+  val = 0 # first index per column
+  transformed_col_vals = [] # new numeric datapoints
   
-    # if not, for each item in that column...
-    for (row, item) in enumerate(colval.values):
+  # if not, for each item in that column...
+  for (row, item) in enumerate(colval.values):
       
-      # if item is not in this col's dict...
-      if item not in new_dict:
-        new_dict[item] = val
-        val += 1
+    # if item is not in this col's dict...
+    if item not in new_dict:
+      new_dict[item] = val
+      val += 1
       
-      # then add numerical value to transformed dataframe
-      transformed_col_vals.append(new_dict[item])
+    # then add numerical value to transformed dataframe
+    transformed_col_vals.append(new_dict[item])
     
-    # reverse dictionary only for final col (0, 1) => (vals)
-    if colname == final_colname:
-      new_dict = {value : key for (key, value) in new_dict.items()}
+  # reverse dictionary only for final col (0, 1) => (vals)
+  if colname == final_colname:
+    new_dict = {value : key for (key, value) in new_dict.items()}
   
-    cat_value_dicts[colname] = new_dict
-    data[colname] = transformed_col_vals
+  cat_value_dicts[colname] = new_dict
+  data[colname] = transformed_col_vals
     
     
   ### -------------------------------- ###
   ###           model training         ###
   ### -------------------------------- ###
   
-  
+def train_model():
   # select features and predicton; automatically selects last column as prediction
   cols = len(data.columns)
   num_features = cols - 1